Eddy Covariance (EC) flux towers allow direct, high-frequency measurements of land-atmosphere carbon (C) exchange, providing a unique opportunity to evaluate C cycle processes in terrestrial ecosystems.
In grasslands — one of the most expansive and productive biomes covering over 40% of the Earth’s land surface yet highly heterogeneous — EC observations offer valuable insight into gross primary productivity (GPP) and its variability across climatic gradients.
However, global GPP estimates and underpinning EC flux datasets remain heavily weighted toward temperate regions, leaving tropical grasslands underrepresented and introducing uncertainty into global C estimates.
This study synthesised EC flux data at ~20 grassland sites predominantly in the tropical regions in Australia with publicly available grassland EC flux datasets including FLUXNET 2015, AMERIFLUX and ICOS, together with meteorological and remote sensing data to improve GPP estimation.
## Reading layer `WorldGrasslandTypes2014' from data source
## `C:\Users\takeda2\OneDrive - Queensland University of Technology\ec_flux_ml\WorldGrasslandTypes\WorldGrasslandTypes2014.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 145 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -13150580 ymin: -5957072 xmax: 18405740 ymax: 7537678
## Projected CRS: World_Goode_Homolosine_Land
## Reading layer `ibra7_regions' from data source
## `C:\Users\takeda2\daycent_r\au_bioregion\ibra7_regions.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 89 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 72.57738 ymin: -54.77699 xmax: 167.9981 ymax: -9.14129
## Geodetic CRS: GDA94
## [1] "Sourced from meteorology data"
## [1] "Sourced from the flux tower"
##
## Call:
## lm(formula = gpp ~ rain_rs15 * swrad_tower, data = df_flux_met_lsat_global_grass_filtered)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.4928 -1.9649 -0.8652 1.3499 21.8905
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.301e-01 2.373e-02 26.55 <2e-16 ***
## rain_rs15 7.159e-03 5.364e-04 13.35 <2e-16 ***
## swrad_tower 6.445e-03 1.114e-04 57.88 <2e-16 ***
## rain_rs15:swrad_tower 1.708e-04 2.494e-06 68.46 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.225 on 165382 degrees of freedom
## (924091 observations deleted due to missingness)
## Multiple R-squared: 0.251, Adjusted R-squared: 0.251
## F-statistic: 1.847e+04 on 3 and 165382 DF, p-value: < 2.2e-16
## [1] "R-squared"
## [1] 0.5944789
## [1] "RMSE (gC/m2)"
## [1] 2.782233
## [1] "RRMSE (%)"
## [1] 93.20144
## [1] "PBIAS (%)"
## [1] -37.49346
| Parameter | Default value | Lower limit | Upper limit |
|---|---|---|---|
| lue_max | 0.86 | 0.5 | 3 |
| tmin_min | -8.00 | -25.0 | 0 |
| tmin_max | 12.02 | 5.0 | 30 |
| vpd_min | 650.00 | 50.0 | 1000 |
| vpd_max | 5300.00 | 1500.0 | 6500 |
## [1] "R-squared"
## [1] 0.652315
## [1] "RMSE (gC/m2)"
## [1] 2.252509
## [1] "RRMSE (%)"
## [1] 74.74779
## [1] "PBIAS (%)"
## [1] -2.791108
## [1] "R-squared"
## [1] 0.7331842
## [1] "RMSE (gC/m2)"
## [1] 382.1422
## [1] "RRMSE (%)"
## [1] 47.99393
## [1] "PBIAS (%)"
## [1] -1.899277
For inquiries or collaborations, feel free to email me or connect via GitHub - the GitHub Issues page allows open discussion.